36 citations · 84 across the 9 of their papers we have counts for
10 papers
SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop
Friedhelm Hamann, Emil Mededovic, Fabian Gülhan +21
We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accur…
Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras
Shuang Guo, Friedhelm Hamann, Guillermo Gallego
Event cameras rely on motion to obtain information about scene appearance. This means that appearance and motion are inherently linked: either both are present and recorded in the…
ETAP: Event-based Tracking of Any Point
Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto +2
Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global im…
Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras
Friedhelm Hamann, Suman Ghosh, Ignacio Juarez Martinez +3
Event cameras are novel bio-inspired vision sensors that measure pixel-wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantag…
MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice
Friedhelm Hamann, Hanxiong Li, Paul Mieske +2
Enabled by large annotated datasets, tracking and segmentation of objects in videos has made remarkable progress in recent years. Despite these advancements, algorithms still strug…
Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation
Friedhelm Hamann, Ziyun Wang, Ioannis Asmanis +3
Current optical flow and point-tracking methods rely heavily on synthetic datasets. Event cameras are novel vision sensors with advantages in challenging visual conditions, but sta…
Low-power, Continuous Remote Behavioral Localization with Event Cameras
Friedhelm Hamann, Suman Ghosh, Ignacio Juarez Martinez +3
Researchers in natural science need reliable methods for quantifying animal behavior. Recently, numerous computer vision methods emerged to automate the process. However, observing…
Event-based Continuous Color Video Decompression from Single Frames
Ziyun Wang, Friedhelm Hamann, Kenneth Chaney +3
We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream. Conventional cameras struggle with high-speed m…
Event-based Background-Oriented Schlieren
Shintaro Shiba, Friedhelm Hamann, Yoshimitsu Aoki +1
Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding. However, conventional frame-based techniques…
Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras
Friedhelm Hamann, Guillermo Gallego
This work introduces a co-capture system for multi-animal visual data acquisition using conventional cameras and event cameras. Event cameras offer multiple advantages over frame-b…